Kim, J (2022) Improved Robustness Analysis of Reinforcement Learning Embedded Control Systems. In: Lecture Notes in Networks and Systems. RiTA 2021: International Conference on Robot Intelligence Technology and Applications, 16-17 Dec 2022, Daejeon, Korea. Springer , pp. 104-115. ISBN 9783030976712
Abstract
Reinforcement learning emerges as an efficient tool to design control algorithms for nonlinear systems. There are, however, few results available on how the robustness of the closed-loop dynamics with reinforcement learning is performed. While μ
-analysis is well established as the robustness analysis tool for linear systems, there is also a limitation caused by ignoring the equilibrium shift by the uncertain parameters. An improved linearisation method for μ
-analysis is presented and the method is applied to the inverted-pendulum system with the reinforcement learning control. The resulting robustness analysis provides a significantly less conservative upper bound to the smallest worst-case perturbation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author produced version of an article published in Lecture Notes in Networks and Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Robustness analysis; Reinforcement learning; Inverted-pendulum |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Funding Information: | Funder Grant number Korea Foundation Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Jan 2023 16:34 |
Last Modified: | 19 Jan 2023 01:13 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-97672-9_10 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195159 |